The new MAS risk management guidelines and how machine learning can help The central bank of Singapore says a rules-based approach to cyber-risk management is no longer sufficient and that banks need ...

How to overcome overfitting in machine learning based fraud mitigation for banks AI fraud-mitigation solutions often focus on a few types of fraud and become incapable of spotting new ones – known as ...

Real-time fraud mitigation for Hong Kong’s virtual banks The new virtual banks about to set up shop in Hong Kong need machine-learning technology to protect them, their customers and the regulator ...

What to expect when implementing NetGuardians With pre-configured adaptors and software, NetGuardians’ fraud-mitigation software could not be easier to implement, allowing banks to prevent more fraud ...

Machine learning can stamp out internal banking fraud By analyzing the behavior of staff, customer and the transaction itself, banks can spot and stop not only known fraud types but also new frauds, ...

NetGuardians’ global take on bank fraud The final part of our series looking at regional fraud offers a global overview highlighting the differences – and similarities – in the threats that banks ...

African banks vulnerable to financial fraud The lack of a robust anti-fraud culture across Africa is responsible for the continent’s position as the number one targeted region for banking fraud Five ...

Outlines for a tech-led solution in the fight against digital banking fraud behavioral data analytics and machine learning hold the key In our previous blog post and in our eBook, we’ve defined the ...

Making real-time fraud detection and prevention possible A combination of powerful technologies ensures NetGuardians’ NG|Screener data analytics risk platform can detect and prevent fraud in real ...

Four bank fraud trends particular to Europe Whether it’s rogue relationship managers in private banking, professional cyber criminals from Eastern Europe or banks’ inability to spot contactless ...